AI Platform DevOps & Observability Lead

EY; Washington (District of Columbia); Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Hybrid work model

Medical and dental coverage

401(k) plans

Job summary

EY seeks an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of EY’s AI-native platform across cloud, on-prem, edge, and air-gapped environments. You will build automated delivery pipelines and secure model execution while ensuring repeatable, auditable deployments.

This role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring strong GPU inference experience, cost visibility, and governance through open telemetry and registries.

Qualifications

• Bachelor's or Master's degree in Computer Science or related field.
• 8+ years in DevOps, MLOps, platform, or observability engineering, with hands‑on production ownership of AI or high‑throughput services.
• Strong hands‑on DevOps experience, including CI/CD/CV pipelines and GitOps tooling (ArgoCD, Helm, GitHub Actions/GitLab CI, or equivalents) for automated build, test, release, and rollback.
• Hands‑on expertise operating inference/model-serving frameworks (Ray Serve, vLLM, Triton, or NIM) on GPU infrastructure.
• Strong experience with observability stacks (Prometheus, Grafana, Loki, Tempo/Jaeger) and OpenTelemetry.
• Experience with API gateways and request routing (Envoy or equivalent), including streaming responses.
• Experience with cost management / FinOps tooling (OpenCost, Kubecost, or equivalent) and quota/rate-limit enforcement.
• Familiarity with model/artifact registries and supply‑chain scanning (Harbor, MLflow, Trivy/SBOM).
• Proven track record operating AI or service infrastructure under compliance, security, or regulatory constraints.

Responsibilities

• Own DevOps and delivery for AI workloads: build and operate the CI/CD/CV pipelines that ship AI services, agents, and runtime components, including automated build, test, continuous verification, release, and rollback, so AI workloads are delivered repeatably and safely into every environment.
• Own governance and discovery for AI assets, including service catalog/registry (Artifactory/Nexus, Harbor), experiment tracking and model metadata (MLflow), upstream registries/mirrors (HuggingFace/NGC), CVE/SBOM scanning (Trivy), lineage contracts (OpenLineage), and license management.
• Own resource and cost management, including quotas and rate limits, cost attribution and utilization (Apptio/OpenCost/Kubecost), so AI execution stays economically bounded and controllable per tenant and engagement.
• Own the full observability stack, including metrics (Prometheus/Mimir), logs (Loki), traces (Tempo/Jaeger), dashboards (Grafana), LLM debugging and evaluation (LangSmith/Langfuse), and SLA/alert notifications.
• Own the OpenTelemetry collection layer, including multi-tenant receiver, exporters and queues (Kafka sink), DCGM exporter for GPU telemetry, processor batching, and dynamic filtering, so every signal is captured and routed reliably.
• Automate GitOps-based delivery and continuous verification; embedding quality, integrity, and cost gates into pipelines so releases are policy-compliant by default rather than by manual review.
• Close the loop between delivery and observability by using telemetry, evaluation, and cost signals to drive deployment decisions, progressive rollout, and automated rollback of AI workloads.
• Ensure cost and telemetry are identity-stamped and per-tenant, so consumption and behavior are attributable end-to-end, keeping FinOps and observability tied to the workloads that generate the load.

Skills

DevOps

MLOps

Observability

OpenTelemetry

Cost optimization

Security/compliance

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Prometheus

Grafana

Loki

Tempo/Jaeger

Envoy

Trivy

Harbor

Job description

EY seeks an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of EY’s AI-native platform across cloud, on-prem, edge, and air-gapped environments. You will build automated delivery pipelines and secure model execution while ensuring repeatable, auditable deployments.

This role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring strong GPU inference experience, cost visibility, and governance through open telemetry and registries.